In this paper we address the problem of open-vocabulary object detection. At training time, the model has access to the detection labels of \(C_B\)  base categories, but needs to detect objects from a set of \(C_N\)  novel categories at test time. To make the settings more practical [1]}, we follow previous works and assume the availability of a pretrained vision and language model (VLM) which has learned from plenty of image-text pairs on the internet [2]}, [3]}.
